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All evaluations

AA-Omniscience: Knowledge and Hallucination Benchmark

A benchmark measuring factual recall and hallucination across various economically relevant domains.

Background

AA-Omniscience is a knowledge and hallucination benchmark that rewards accuracy, punishes bad guesses and provides a comprehensive view of which models produce factually reliable outputs across different domains.
The benchmark contains 6,000 questions across 6 major domains, derived from authoritative academic and industry sources and generated automatically using an LLM-based question generation agent to ensure unambiguity, scalability and factual precision

Methodology

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

AA-Omniscience: Evaluating Cross-Domain Knowledge Reliability in Large Language Models

Declan Jackson, William Keating, George Cameron, Micah Hill-Smith.

Related links

2511.130292511.13029ArtificialAnalysis/AA-Omniscience-PublicArtificialAnalysis/AA-Omniscience-Public

Highlights

  • Gemini 3.1 Pro Preview scores the highest on AA-Omniscience with a score of 33, followed by Gemini 3 Pro Preview (high) with a score of 16, and Claude Opus 4.6 (Adaptive Reasoning, Max Effort) with a score of 14
  • Gemini 3 Pro Preview (high) scores the highest on AA-Omniscience Accuracy with a score of 56%, followed by Gemini 3.1 Pro Preview with a score of 55%, and Gemini 3 Flash Preview (Reasoning) with a score of 54%
  • Grok 4.20 Beta 0309 (Reasoning) scores the lowest on AA-Omniscience Hallucination Rate with a score of 22%, followed by Claude 4.5 Haiku (Non-reasoning) with a score of 25%, and Grok 3 mini Reasoning (high) with a score of 25%

AA-Omniscience Index: Results

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
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Independently benchmarked by Artificial Analysis

AA-Omniscience Index

AA-Omniscience Index vs. Artificial Analysis Intelligence Index

AA-Omniscience Index; Artificial Analysis Intelligence Index
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Most attractive quadrant
Alibaba
Amazon
Anthropic
DeepSeek
Google
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
MiniMax
Mistral
NVIDIA
OpenAI
xAI
Xiaomi
Z AI

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

AA-Omniscience Accuracy

AA-Omniscience Accuracy

AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer
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Reasoning models are indicated by a lightbulb icon.

AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer

AA-Omniscience Hallucination Rate

AA-Omniscience Hallucination Rate

AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted).
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Reasoning models are indicated by a lightbulb icon.

AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted).

Detailed Domain Results

AA-Omniscience Index Across Domains (Normalized)

AA-Omniscience Index; Scores are normalized per domain across all models tested, where green represents the highest score for that domain and red represents the lowest score for that domain.
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Reasoning model

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Software Engineering Deep Dive

Software Engineering AA-Omniscience Index Across Languages (Normalized)

Software Engineering AA-Omniscience Index; Scores are normalized per language across all models tested, where green represents the highest score for that language and red represents the lowest score for that language.
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Reasoning model

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

AA-Omniscience Index Question Breakdown

Distribution of questions across different domains and subdomains in the AA-Omniscience benchmark

Business

Humanities & Social Sciences

Science, Engineering & Mathematics

Health

Law

Software Engineering (SWE)

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Model Size (Open Weights Models Only)

AA-Omniscience Index vs. Total Parameters

AA-Omniscience Index; Size in Parameters (Billions)
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Most attractive quadrant
Alibaba
DeepSeek
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
Mistral
NVIDIA
OpenAI
Xiaomi
Z AI

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

AA-Omniscience Accuracy vs. Total Parameters

AA-Omniscience Accuracy; Size in Parameters (Billions)
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Most attractive quadrant
Alibaba
DeepSeek
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
Mistral
NVIDIA
OpenAI
Xiaomi
Z AI

AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

AA-Omniscience Hallucination Rate vs. Total Parameters

AA-Omniscience Hallucination Rate; Size in Parameters (Billions)
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Most attractive quadrant
Alibaba
DeepSeek
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
Mistral
NVIDIA
OpenAI
Xiaomi
Z AI

AA-Omniscience Hallucination Rate (lower is better) measures how often the model answers incorrectly when it should have refused or admitted to not knowing the answer. It is defined as the proportion of incorrect answers out of all non-correct responses, i.e. incorrect / (incorrect + partial answers + not attempted).

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

AA-Omniscience Index: Token Usage

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Tokens used to run the evaluation
Input tokens
Reasoning tokens
Answer tokens

The total number of tokens used to run the evaluation, including input tokens (prompt), reasoning tokens (for reasoning models), and answer tokens (final response).

AA-Omniscience Index: Cost Breakdown

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Cost (USD) to run the evaluation
Input cost
Reasoning cost
Answer cost

The cost to run the evaluation, calculated using the model's input and output token pricing and the number of tokens used.

AA-Omniscience Index: Score vs. Release Date

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Most attractive region
Alibaba
Amazon
Anthropic
DeepSeek
Google
Kimi
Korea Telecom
LG AI Research
MBZUAI Institute of Foundation Models
Meta
MiniMax
Mistral
NVIDIA
OpenAI
xAI
Xiaomi
Z AI

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